Machine Learning : A Theoretical Approach

This is the first comprehensive introduction to computational learning theory. The author's uniform presentation of fundamental results and their applications offers AI researchers a theoretical perspective on the problems they study. The book presents tools for the analysis of probabilistic models of learning, tools that crisply classify what is and is not efficiently learnable. After a general introduction to Valiant's PAC paradigm and the important notion of the Vapnik-Chervonenkis dimension, the author explores specific topics such as finite automata and neural networks. The presentation is intended for a broad audience--the author's ability to motivate and pace discussions for beginners has been praised by reviewers. Each chapter contains numerous examples and exercises, as well as a useful summary of important results. An excellent introduction to the area, suitable either for a first course, or as a component in general machine learning and advanced AI courses. Also an important reference for AI researchers. Content: Copyright, Page ii Front Matter, Page iii Preface, Pages ix-x 1 - Introduction, Pages 1-5 2 - Learning Concepts on Countable Domains, Pages 7-40 3 - Time Complexity of Concept Learning, Pages 41-72 4 - Learning Concepts on Uncountable Domains, Pages 73-97 5 - Learning Functions, Pages 99-124 6 - Finite Automata, Pages 125-146 7 - Neural Networks, Pages 147-166 8 - Generalizing the Learning Model, Pages 167-196 9 - Conclusion, Pages 197-202 Notation, Pages 203-205 Bibliography, Pages 207-214 Subject Index, Pages 215-217